{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise was created based on the tutorial and documentation from [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html)  \n",
    "The dataset being used is tips from Seaborn.\n",
    "\n",
    "### Step 1. Import the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Tips/tips.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Exercises_with_code_and_solutions.ipynb',\n",
       " 'Solutions.ipynb',\n",
       " 'tips.csv',\n",
       " '.ipynb_checkpoints',\n",
       " 'Exercises.ipynb']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = pd.read_csv('tips.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Delete the Unnamed 0 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  total_bill   tip     sex smoker  day    time  size\n",
       "0           0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1           1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2           2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3           3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4           4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del tips['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Plot the total_bill column histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on package matplotlib.style in matplotlib:\n",
      "\n",
      "NAME\n",
      "    matplotlib.style\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    core\n",
      "\n",
      "DATA\n",
      "    available = ['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic...\n",
      "    library = {'Solarize_Light2': RcParams({'axes.axisbelow': True,\n",
      "          ...\n",
      "\n",
      "FILE\n",
      "    /root/miniconda3/lib/python3.8/site-packages/matplotlib/style/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TxhH8twNLkjwryVOBVwMbxlCHJDVp5Es9VfV4kj8G/hWYB3yoqr426jqOYM0tb+3B8bet5fGPbOyp2mt5XZJ0DPObu5LUGINfkhpj8I9Rkg8l2ZXk3ll985NsTLK12542zhr7kuSsJF9KsiXJ15Jc0/W3Mv4TktyW5D+78f9519/E+HdLMi/JXUk+27WbGX+SbUnuSXJ3ks1d30jGb/CP10eAy/boWw1sqqolwKaufSx6HHhTVT0HuAh4Q3fpjlbG/yjw4qo6D1gKXJbkItoZ/27XAFtmtVsb/yVVtXTW5/dHMn6Df4yq6ivA9/foXg5Md/vTwIpR1jQqVbWjqu7s9h9m8Mt/Bu2Mv6rqka55fPdTNDJ+gCRnApcDH5jV3cz492Ek4zf4jzwLq2oHDMIRWDDmenqXZBI4H7iVhsbfLXPcDewCNlZVU+MH3gu8GfjZrL6Wxl/A55Pc0V2iBkY0fm+2rrFK8jTgM8Abq+qhZK4rehybquoJYGmSU4Ebkzx3zCWNTJJXALuq6o4kLxpzOeNycVVtT7IA2JjkvlGd2Bn/kWdnkkUA3XbXmOvpTZLjGYT+J6rqhq67mfHvVlU/BG5h8H5PK+O/GLgiyTYGV+h9cZKP0874qart3XYXcCODKxePZPwG/5FnA7Cy218JrB9jLb3JYGr/QWBLVb1n1kOtjH+im+mT5ETgJcB9NDL+qnprVZ1ZVZMMLtvyxap6LY2MP8nJSU7ZvQ+8FLiXEY3fb+6OUZJPAS9icDnWncA7gJuAdcBi4H7gyqra8w3go16SFwL/BtzDz9d4r2Wwzt/C+J/H4M27eQwmYOuq6i+SPIMGxj9bt9TzZ1X1ilbGn+RsBrN8GCy5f7Kq3jWq8Rv8ktQYl3okqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWrM/wEJNdSphYADPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips.total_bill.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatter plot presenting the relationship between total_bill and tip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb06534d6a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=tips.total_bill, y=tips.tip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.  Create one image with the relationship of total_bill, tip and size.\n",
    "#### Hint: It is just one function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Step 8. Present the relationship between days and total_bill value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a scatter plot with the day as the y-axis and tip as the x-axis, differ the dots by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Create a box plot presenting the total_bill per day differetiation the time (Dinner or Lunch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Create two histograms of the tip value based for Dinner and Lunch. They must be side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Create two scatterplots graphs, one for Male and another for Female, presenting the total_bill value and tip relationship, differing by smoker or no smoker\n",
    "### They must be side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it using a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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